Radiologically identified tumor habitats

ABSTRACT

Virtually every cancer patient is imaged with CT, PET or MRI. Importantly, such imaging reveals that tumors are complex and heterogeneous, often containing multiple habitats within them. Disclosed herein are methods for analyzing these images to infer cellular and molecular structure in each of these habitats. The methods can involve spatially superimposing two or more radiological images of the tumor sufficient to define regional habitat variations in two or more ecological dynamics in the tumor, and comparing the habitat variations to one or more controls to predict the severity of the tumor.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of U.S. Provisional Application No.61/950,711, filed Mar. 10, 2014, and U.S. Provisional Application No.Application Ser. No. 61/955,067, filed Mar. 18, 2014, which are herebyincorporated herein by reference in their entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government Support under Grant No.CA143970, CA143062, CA077575, CA17059, and CA076292 awarded by theNational Institutes of Health. The Government has certain rights in theinvention.

BACKGROUND

Intratumoral and intertumoral heterogeneities are well recognized atmolecular, cellular, and tissue scales [Yancovitz M, et al. (2012). PLoSOne 7, e29336; Inda M M, et al. (2010). Genes Dev 24, 1731-1745;Gerlinger M, et al. (2012). N Engl J Med 366, 883-892; Sottoriva A, etal. (2013). Proc Natl Acad Sci USA 110, 4009-4014; Marusyk A, et al.(2012). Nat Rev Cancer 12, 323-334; Yachida S, et al. (2010). Nature467, 1114-1117]. This is clearly evident in the imaging characteristicsof glioblastoma multiforme, which typically include regions of high andlow contrast enhancement as well as high and low interstitial edema andcell density. Several recent molecular studies have demonstrated thatthere is also significant genetic variation among cells in differenttumors and even in different regions of the same tumor [Gerlinger M, etal. (2012). N Engl J Med 366, 883-892; Sottoriva A, et al. (2013). ProcNatl Acad Sci USA 110, 4009-4014; Marusyk A, et al. (2012). Nat RevCancer 12, 323-334]. In one study, for example, samples from spatiallyseparated sites within kidney tumors found that multiple molecularsubtypes were present in all of the examined tumors [Gerlinger M, et al.(2012). N Engl J Med 366, 883-892]. It is clear that this molecularheterogeneity may significantly limit efforts to personalize cancertreatment based on the use of molecular profiling to identify druggabletargets [Gerlinger M and Swanton C (2010). Br J Cancer 103, 1139-1143;Mirnezami R, et al. (2012). N Engl J Med 266, 489-490; Kern S E (2012).Cancer Res 72, 1-5]. However, there has, thus far, been little effort torelate the spatial heterogeneity observed in clinical imaging with thegenetic heterogeneity found in molecular studies.

SUMMARY

Virtually every cancer patient is imaged with CT, PET, or MRI.Importantly, such imaging reveals that tumors are complex andheterogeneous, often containing multiple “habitats” within them. Methodsfor analyzing these images to infer cellular and molecular structure ineach of these habitats are disclosed.

Disclosed is a radiological method for predicting the severity of atumor in a subject that involves spatially superimposing two or moreradiological images of the tumor sufficient to define regional habitatvariations in two or more ecological dynamics in the tumor, andcomparing the habitat variations to one or more controls to predict theseverity of the tumor. In some cases, the method predicts the survivalof the subject based on the severity of the tumor. The methods can alsobe used to monitor and/or predict a therapy response. The methods cantherefore further involve selecting the appropriate treatment for thesubject based on the predicted severity of the tumor, e.g., palliativecare or adjuvant treatment.

The tumor can be any tumor for which radiological imaging is available.In particular embodiments, the tumor is a glioblastoma multiforme (GBM),prostate cancer, pancreatic cancer, renal cell carcinoma, myxoid tumor,or soft tissue sarcoma.

The methodology described here will produce 3D habitat maps of theanalyzed organs which can be used for diagnosis, prognosis, therapyselection, and evaluating response to therapy. This will allow treatmentto be individualized for patients based on the tumor habitats. Forexample, the methods can be used to identify normal and pathologicaltissue in a patient with GBM and monitor the response of the patient toimmunotherapy by detecting immune filtration into the tumor. The methodscan be used to diagnose the percentage of sarcomatoid differentiation ina renal cell carcinoma. The methods can also be used to diagnose benignvs. malignant myxoid tumor (e.g., benign intramuscular myxoma vs.malignant myxoid lipsarcoma).

In some embodiments, at least one of the two or more ecological dynamicscomprises perfusion (blood flow). In these and other embodiments, atleast one of the two or more ecological dynamics comprises interstitialcell density (edema). In these and other embodiments, at least one ofthe two or more ecological dynamics comprises extracellular pH (pHe). Insome embodiments, at least one of the two or more ecological dynamicscomprises hypoxia.

Any radiological method suitable to ascertain ecological dynamics of thetumor can be used. For example, the radiological images can be obtainedby a magnetic resonance imaging (MRI) sequence. Examples of suitable MRIsequences include longitudinal relaxation time (T1)-weighted images(e.g., pre-contrast or post-contrast; with or without fat suppression),transverse relaxation time (T2)-weighted images, T2*-weightedgradient-echo images, fluid attenuated inversion recovery (FLAIR), ShortTau Inversion Recovery (STIR), perfusion imaging, Arterial Spin Labeling(ASL), diffusion tensor imaging (DTI), and Apparent DiffusionCoefficient of water (ADC) maps.

In some cases, rigid-body and/or elastic image registration can be usedto render images from the different MRI sequences superimposable in 3Dprior to performing the analysis.

In some embodiments, the regional habitat variations are defined using afuzzy clustering algorithm analysis of the radiological images. Forexample, pixels can be classified of into clusters using a variation ofthe decision tree depicted in FIG. 28. In some embodiments, the regionalhabitat variations are defined using a thresholding algorithm (e.g.Otsu) analysis of the radiological images.

In some embodiments, the disclosed method separates tumor pixels intodistinct clusters representing benign or malignant transformation.

In some embodiments, detection of relatively low heterogeneity inregional habitats is an indication of low severity of the tumor; whereasdetection of relatively high heterogeneity in regional habitats is anindication of high severity of the tumor. In some cases, detection ofrelatively high cell density and relatively high perfusion in the tumoris an indication of low severity of the tumor.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1. An example of an analysis from a single axial plane MRI imagefrom a patient with GBM. For the first row: (A) Boundary outlines thetumor region including enhancement and non-enhancement. (B, C) Thecorresponding axial plane FLAIR and T2 scans, respectively. The secondrow illustrates the associated pixel histogram distribution (tumorregion).

FIG. 2. Survival time distribution demonstrating a broad scale from 16to 1730 days.

FIG. 3. Two-dimensional histogram distribution. In the responding group,the distribution of normalized values of T1 post-gadolinium,T2-weighted, and FLAIR images was plotted, respectively. Each group wasshown as a normalized cumulative histogram.

FIG. 4. In the top Figure, the frequency of the normalized values of allT1 post-gadolinium images was plotted. Using a Gaussian mixture mode,the histogram was divided into two Gaussian populations with aseparation point of 0.26. The normalized FLAIR signal was then plottedin the high and low groups. The result suggests that GBMs consist offive dominant habitats—two with high blood flow and high and low celldensity and three with low blood flow and high, low, and intermediatecell densities.

FIG. 5. Three-dimensional histogram distribution shows the relativedistribution of combinations of perfusion and cell density in the twogroups of GBMs. For each group, we plotted the joint cumulative 3Dhistogram by summing all cases of 3D histograms of each class. The thirddimension is the frequency distribution. Group 2 cases are relativelyhomogenous with most regions clustering in regions of high perfusion andintermediate cell density. Group 1 cases show greater heterogeneity withQ9 more areas of decreased perfusion with mixed cell density.

FIG. 6. Prediction results. Both leave-one-out and 10-foldcross-validation schemes were used to validate the predictionperformance.

FIG. 7. The block diagram of spatial mapping. For input brain tumor data(e.g., T1-weighted and FLAIR), two intratumor habitats were obtained byusing the nonparametric Otsu algorithm from each modality separately (asseen from blue and red dashed curves). The following spatial mappingprocedure was conducted by an intersection operation. The visualhabitats are shown in the last block with color codes.

FIG. 8. Osteosarcoma Xenotransplant. (A) H&E of whole mount. (B)individual cells were segmented and clustered according to Haralicktextures of nuclei, nuclear size and eosin density features to identifysub-regions with similar morphotypes.

FIG. 9. FSE (L) and ADC map (R) of osteosarcoma xenotransplants in SCIDmouse. Bar=8 mm.

FIG. 10. MR imaging of “Habitats” in PDAC (bars=1 cm). Contrast-EnhancedT1w and ADC maps were obtained in both (A) clinical and (B) preclinical(MiaPaCa xenograft) subjects. The lowest quartile of T1 weighted mapswas used as a mask to mark volumes with the lowest blood flow/perfusion.Within this, the areas within the (A) highest quartile of ADC (lowestcell density) were masked in green to mark “NECROSIS”; and (B) lowestquartile of ADC (highest cell density) were masked in purple to mark“HYPDXIA”.

FIG. 11. Clinical STS Images: Each pixel value in the tumor is plottedon axes representing each sequence (T1, STIR/T2, post contrast). UsingFuzzy c-means clustering technique, data points are clustered. Eachcluster is assigned a color and then presented in 2D space on the tumormask as a color segmentation map.

FIG. 12. Response of HT1080 Sarcoma to TH-302+DOX with standard dosing.

FIG. 13. Pixel-by-pixel histograms demonstrating the ADC distributionpre- (blue) and post-treatment on day 2 (red) for select animals fromall four treatment groups and corresponding quantitative analysis ofpercent change (average) in relative ADC.

FIG. 14. (A) ADC-entropy plots as obtained by texture-based analysis ofpost-treatment ADC-maps herein demonstrating the evident differences inADC values and distribution within the perimeters of the tumor (seecolor bar). (B) Corresponding change in average entropy values frompre-treatment for all four groups displaying statistically largervariations in ADC values for the Gem group compared to both controls(p=0.022) and MK1775 (p=0.023).

FIG. 15. Oxygen consumption increases in response to 2 mM pyruvate inHs766t, MiaPaCa and Su.86.86 PDAC cell lines.

FIG. 16. Decreased intratumoral oxygenation 1 hr following 2 mmol/kgpyruvate in PDAC xenografts.

FIG. 17. Pyruvate improves survival associated with TH-302 therapy inMIA PaCa-2 xenografts

FIG. 18. (A) T2w Axial slice (B) Early enhancement DCE (C) tumorcontrast-to-time pattern; (D) ADC map; (E) Volumes of high perfusion(red) and low ADC (yellow) displayed in MIM.

FIG. 19. (left to right) Axial slice of prostate on T2w; DCE map; ADCmap; Combination with low ADC values (green) and ADC-DCE overlap inyellow, displayed in MIM.

FIG. 20. Schematic representation of the prostate and the biopsylocations. The shaded area in the upper right of the prostate contourdepicts the SImTV. The symbols in blue represent the location of biopsysampling in the regions not suspicious for tumor. Note that in theregions suspicious for tumor, the biopsies will target the suspiciouslesion (red symbols). The green represents an extra biopsy from thisarea (up to 14 biopsies permitted).

FIG. 21. MP-MRI findings and directed prostate biopsy of the indexlesion. (A) T2w; (B) ADC map (arrow indicating tumor); (C) DCE intensitymap (D) Tumor (yellow) and prostate (brown). (E) 3D volumes transferredto Artemis™ for fusion with real-time ultrasound; (F) biopsy needlepath.

FIG. 22. Histogram of the T2 intensities in the prostate of a patientwith prostate cancer. Otsu and mean±σ thresholds.

FIG. 23. Habitats, based on intersections between the T2, ADC and DCE(left) and ADC and DCE (right).

FIG. 24. An irregular shaped tumor on ADC map.

FIG. 25. QH of Prostate Cancer. Two Gleason 6 core biopsies wereanalyzed by segmenting individual cells from H&E images (left) andclassifying them (right) as stroma (blue) or cancer (orange)illustrating range of stromal involvement.

FIG. 26. pH imaging. (A) pH image from MRSI of IEPA of MDA-mb-435 mousexenografts, overlaid on contrast-enhanced image of same. (B) CSI matrixobtained 20-40 mins after injection of IEPA, delineating region of tumorin red circle. (C) from co-registered DCE and pH data, relationshipbetween pHe to the time to maximal intensity, TMI, showing lower pH withless perfusion.

FIG. 27. Co-hyperpolarization of 13C bicarbonate and ¹³C-1-Pyruvic Acid.Stacks were generated from a series of 2 sec spectra showing decay ofpyruvate (and hydrate) bicarb and CO₂.

FIG. 28. Example decision tree using MRI sequences to identify distincthabitats within the entire brain. Co-registered T1-weightedpre-contrast, T1-weighted post-contrast, T2-weighted pre-contrast,FLAIR, and Apparent Diffusion Coefficient of water (ADC) maps and abinary brain mask were used as inputs to the algorithm depicted here. Ateach decision point in the algorithm, voxels are classified into eithertwo or three clusters using Otsu thresholding. Note that the brighterclusters are shown to the right. Through the combination of MRIsequences, volumes of interest (VOIs) of the habitats were continuouslyrefined, and a final habitat type identified for each of the 10 habitatsin this decision tree.

FIG. 29. Whole brain images from T1-weighted post-contrast (top row,left panel) and FLAIR (top row, middle panel) sequences acquired on apatient with a recurrent Glioblastoma multiforme (GBM) tumor. A 3Dmulti-parametric tumor habitat map was constructed as per the method inFIG. 28 (top row, right panel). A zoomed-in region of the habitatswithin the tumor is shown in the left panel of the bottom row, with eachcolor representing a distinct “habitat”. A 3D rendering of these samehabitat maps (bottom row, right panel) illustrates the spatial coherencyof the habitats identified by this method.

DETAILED DESCRIPTION

The disclosed methods involve generating radiologically defined“habitats” by spatially superimposing at least two differentradiological sequences from the same tumor. The radiological images ofthe disclosed methods may be scanned images obtained using any suitableimaging techniques. Typical imaging techniques include magneticresonance imaging (MRI), computerized tomography (CT), positron emissiontomography PET, digital subtraction angiography (DSA), single photonemission computed tomography (SPECT), and the like. The exemplaryprocesses described below will be illustrated with reference to aparticular type of images, MRI images. Suitable MRI images includeT1-weighted (T1), T2-weighted (T2), diffusion-weighted (DWI),perfusion-weighted (FWD, fast fluid-attenuated inversion-recovery(FLAIR), cerebral blood volume (CBV), and echo-planar (EPI) images,apparent diffusion coefficient (ADC) and mean-transit-time (MTT) maps,and the like. However, it is understood that embodiments can be appliedfor registering other combinations of MRI or other types of images. Theimages may be represented digitally using intensity histograms or mapswhere each voxel has a corresponding coordinate and intensity value.

The choice of modalities and mapping procedure can be varied accordingto the needs of the specific task. In short, as a tool for brain tumorheterogeneity analysis, the design of the spatial habitat concept gaverise to various opportunities for quantitative measurement (e.g., usingthese habitats to quantitatively observe tumor evolution progress).

The term “subject” refers to any individual who is the target ofadministration or treatment. The subject can be a vertebrate, forexample, a mammal. Thus, the subject can be a human or veterinarypatient. The term “patient” refers to a subject under the treatment of aclinician, e.g., physician.

The term “treatment” refers to the medical management of a patient withthe intent to cure, ameliorate, stabilize, or prevent a disease,pathological condition, or disorder. This term includes activetreatment, that is, treatment directed specifically toward theimprovement of a disease, pathological condition, or disorder, and alsoincludes causal treatment, that is, treatment directed toward removal ofthe cause of the associated disease, pathological condition, ordisorder. In addition, this term includes palliative treatment, that is,treatment designed for the relief of symptoms rather than the curing ofthe disease, pathological condition, or disorder; preventativetreatment, that is, treatment directed to minimizing or partially orcompletely inhibiting the development of the associated disease,pathological condition, or disorder; and supportive treatment, that is,treatment employed to supplement another specific therapy directedtoward the improvement of the associated disease, pathologicalcondition, or disorder.

The term “radiomics” refers to the extraction and analysis of largeamounts of advanced quantitative MP-MRI features using high throughputmethods.

The tumor of the disclosed methods can be any cell in a subjectundergoing unregulated growth, invasion, or metastasis. In some aspects,the cancer can be any neoplasm or tumor for which radiotherapy iscurrently used. Alternatively, the cancer can be a neoplasm or tumorthat is not sufficiently sensitive to radiotherapy using standardmethods. Thus, the cancer can be a sarcoma, lymphoma, carcinoma,blastoma, or germ cell tumor. A representative but non-limiting list ofcancers that the disclosed compositions can be used to treat includelymphoma, B cell lymphoma, T cell lymphoma, mycosis fungoides, Hodgkin'sDisease, myeloid leukemia, bladder cancer, brain cancer, nervous systemcancer, head and neck cancer, squamous cell carcinoma of head and neck,kidney cancer, lung cancers such as small cell lung cancer and non-smallcell lung cancer, neuroblastoma/glioblastoma, ovarian cancer, pancreaticcancer, prostate cancer, skin cancer, liver cancer, melanoma, squamouscell carcinomas of the mouth, throat, larynx, and lung, colon cancer,cervical cancer, cervical carcinoma, breast cancer, epithelial cancer,renal cancer, genitourinary cancer, pulmonary cancer, esophagealcarcinoma, head and neck carcinoma, large bowel cancer, hematopoieticcancers; testicular cancer; colon and rectal cancers, prostatic cancer,and pancreatic cancer. In particular embodiments, the tumor is aglioblastoma multiforme (GBM).

A number of embodiments of the invention have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the invention.Accordingly, other embodiments are within the scope of the followingclaims.

EXAMPLES Example 1 Glioblastoma Multiforme

Introduction

Temporal and spatial cellular heterogeneities are typically ascribed toclonal evolution generated by accumulating random mutations in cancercell populations [Gerlinger M, et al. (2012). N Engl J Med 366, 883-892;Nowell P C (1976). Science 194, 23-28; Greaves M and Maley C C (2012).Nature 481, 306-313]. However, Darwinian dynamics are ultimatelygoverned by the interactions of local environmental selection forceswith cell phenotypes (not genotypes) [Gatenby R A and Gillies R J(2008). Nat Rev Cancer 8, 56-61; Gatenby R (2012). Nature 49, 55]. Thatis, while mutations may occur randomly, proliferation of that clone willproceed only if its corresponding phenotype is more fit than extantpopulations within the context of the local adaptive landscape. Becauseof this evolutionary triage of each heritable (i.e., genetic orepigenetic) event, intratumoral evolution is fundamentally linked to theregional variations in microenvironmental selection forces thatultimately determine the fitness of any genotype/phenotype [Gatenby R Aand Gillies R J (2008). Nat Rev Cancer 8, 56-61; Gatenby R (2012).Nature 49, 55; Gillies R J, et al. (2012). Nat Rev Cancer 12, 487-493].The Darwinian dynamics that link genetic changes with environmentalconditions will permit the characterization of regional variations inthe molecular properties of cancer cells with environmental conditions(such as blood flow, edema, and cell density) that can be determinedwith clinical imaging [Gatenby R, et al. (2013). Radiology 269, 8-15].

Spatial heterogeneity in tumor characteristics is well recognized incross-sectional clinical imaging (FIG. 1). Many tumors exhibitsignificant regional differences in contrast enhancement along withvariations in cellular density, water content, fibrosis, and necrosis.In clinical practice, this heterogeneity is typically described innonquantitative terms. More recently, metrics [Asselin M C, et al.(2012). Eur J Cancer 48, 447-455; Chicklore S, et al. (2012). Eur J NuclMed Mol Imaging 40, 133-140] of heterogeneity, such as Shannon entropy,have been developed and can be correlated with tumor molecular features[Lambin P, et al. (2012). Eur J Cancer 30, 1234-1248; Nair V S, et al.(2012). Cancer Res 72, 3725-3734; Diehn M, et al. (2008). Proc Natl AcadSci USA 105, 5213-5218; Segal E, et al. (2007). Nat Biotechnol 25,675-680] and clinical outcomes [Tixier F, et al. (2011). J Nucl Med 52,369-378; Pang K K and Hughes T (2003). J Chin Med Assoc 66, 655-661;Ganeshan B, et al. (2012). Eur Radiol 22, 796-802]. However, metricsthat assign a single value to heterogeneity tacitly assume that thetumor is “well mixed” and thus does not capture spatial distributions ofspecific tumor properties.

Disclosed is a spatially explicit approach that identifies andquantifies specific subregions of the tumor based on clinical imagingmetrics that can provide information about the underlying evolutionarydynamics. In this approach, tumors will generally possess subregionswith variable Darwinian dynamics, including environmental selectionforces and phenotypic adaptation to those forces [Gatenby R, et al.(2013). Radiology 269, 8-15]. The approach in this example generatesradiologically defined “habitats” by spatially superimposing twodifferent MRI sequences from the same tumor. The goal in this initialwork is to examine regional variations in perfusion/extravasation basedon T1 post-gadolinium images and interstitial edema/cell densitydetermined by fluid attenuated inversion recovery (FLAIR) and T2 images.Clearly, a full characterization of Darwinian dynamics in intratumorhabitats will require more extensive imaging probably from multiple Q6modalities (i.e., PET, MRI, and CT) or other MRI sequences (particularlyapparent diffusion coefficient maps). Nevertheless, in this preliminarystudy, the following questions were asked: 1) Can GBMs be consistentlydivided into some small number of specific radiologically definedhabitats based on combinations of images sensitive to blood flow andedema? 2) Does the distribution of these regions vary among tumors indifferent survival groups?

This approach applies a unique ecological/evolutionary perspective thatallows clinical imaging characteristics to define regional variations inintratumoral Darwinian dynamics that govern intratumoral molecularheterogeneity. The preliminary studies demonstrate that the distributionof these radiologically defined habitats can be correlated with clinicaloutcomes.

Materials and Methods

Patient Information

A data set of 32 patients was collected between January and December2012 from the publicly available TCGA. Although the database containsmore than 500 cases, full imaging sets were often not available.Patients were included if they 1) had complete MRI studies that includedpost-contrast T1-weighted, FLAIR, and T2-weighted sequences and 2) hadclinical survival time. Patients were excluded if they had multipletumors or if the tumor was too small to analyze (<2 cm in diameter). Asubset of 66 cases (43 cases with less than 400 days survival and 23cases with more than 400 days survival) satisfied these two conditions.In the initial analysis, a balanced data set (FIG. 2) was selected thatincluded 16 cases each in group 1 (survival time below 400 days) andgroup 2 (survival time above 400 days), respectively. The latter 16 werearbitrarily chosen. All of the images had a 200 mm×200 mm field of viewand 5-mm slice thickness, with 256×256 or 512×512 acquisition matrices.To ensure uniform resolution of intravariation of each sequence, foreach case, three channels were registered by bilinear interpolation.Since the enrolled patients were from multiple institutions, the studieswere performed on a wide range of MRI units with some variations intechnique.

Image Normalization

To enable consistent evaluations for all cases, the obtained MRI imagingdata were processed by standardizing the intensity scales [Shah M, etal. (2011). Med Image Anal 15, 267-282]. Linear normalization on eachvolume was employed. The voxels of each volume of the tumor region wereindependently normalized into the scale from 0 to 1. Thus, thenormalization captured the local tumor variations of specific patientsin the standard range.

Tumor Identification

For consistency, the regions of interest were segmented by manuallydrawn masks on the post-gadolinium T1-weighted images as shown inFIG. 1. Although automated tumor segmentation meth-Q7 ods have beendescribed [Clark M C, et al. (1998). IEEE Trans Med Imag 17, 187-201;Prastawa M, et al. (2004). Med Image Anal 8, 275-283], they can beunpredictably inaccurate and appeared to offer no advantage over manualsegmentation in GBMs where tumor edges are characteristically welldefined in T1 post-gadolinium sequences.

Histogram Analysis

For the initial analysis, two-dimensional (2D) histo-Q8 grams (FIGS. 3and 4) of the cumulative voxel intensities were generated for alltumors. To perform a cohort analysis, the frequencies were rescaled intoa range [0,1] using the normalization described above. In thehistograms, the y-axis represents the frequency with which a particularMRI sequence intensity (x-axis) was observed.

In addition, 3D histograms (FIG. 5) were used to visually observevariations in tumor heterogeneity. The x- and y-axes consisted of theavailable pairs of MRI modalities: post-gadolinium T1-weighted andFLAIR, post-gadolinium T1-weighted and T2-weighted, or FLAIR andT2-weighted. A joint histogram that considered the cross-distribution ofeach modality was used. For instance, considering the pair ofpost-gadolinium T1-weighted and FLAIR modalities, an aggregatedhistogram was formed by counting the joint intensity of each voxel(x_(i), x_(j)), where xi was denoted as the T1-weighted intensity signaland x_(j) was the associated FLAIR intensity signal. The z-axisdimension represented the joint distribution of each voxel. Theremaining combinations followed a similar process to obtain the 3Dhistogram representation.

Survival Time Criterion

The clinical survival time (FIG. 2) was defined as the number of daysbetween the date of the initial pathologic diagnosis and the time todeath obtained from the patient demographics in the TCGA database. TheMRI imaging data obtained at the initial diagnosis was used, thus thepossible influence of the following clinical therapy was not accountedfor in this study. Since there was no explicit prior study suggestingthe precise survival threshold for different survival groups, theoverall statistics in the study were approximately followed [Fine H A(1994). J Neurooncol 20, 111-120], where a reported median value ofsurvival time for malignant brain tumor was 12 to 14 months. In thisstudy, the patient cohort was initially divided into two equal groups:group 1 (survival time<400 days) and group 2 (survival time >400 days),thus the criterion used here differs from that used in another study[Sonoda Y, et al. (2009). Acta Neurochir 151, 1349-1358], which definedlong-term survival as more than 3 years (36 months); only 2% to 5% ofpatients were in this group.

Results

Demographic Data

FIG. 2 and Tables 1-3 summarize the clinical and molecular data. clearseparation of the GBM images clear separation of the GBM images survivaltime <400 days were slightly older (mean age 62) and had more totalmutations (n=29) than the group with survival time >400 days (mean age60, total mutations=25). Only limited information was available onmolecular subtype, although recent investigations have shown thatmultiple subtypes are characteristically observed in each tumor[Sottoriva A, et al. (2013). Proc Natl Acad Sci USA 110, 4009-4014]. Themean tumor diameter and overall volume was slightly greater in group 1,but the difference was not statistically significant. All patients weretreated with radiation therapy, chemotherapy, and surgery, althoughdetails were not available.

TABLE 1 Data Set of Demographic Information. Survival < 400 DaysSurvival > 400 Days (n = 16) (n = 16) Age: Range, median 47-80, 6218-78, 60 Sex 8M, 8F 10M, 6F Histology Available in n = 9 Available in n= 11 Classical 1/9 2/11 Mesenchymal 4/9 6/11 Proneural 3/9 2/11 Neural1/9 1/11 Mutations CDKN2A 8 11  EGFR 6 8 PTEN 4 3 PDGFRA 2 2 TP53 3 1CDK4 3 0 NF1 2 1 CDK6 1 0

TABLE 2 Distribution of Gene Mutations in Group 1. Tumor ID CDKN2A EGFRPTEN PDGFRA TP53 CDK4 NF1 CDK6 Total 1 X X X 3 2 X 1 3 X 1 4 X X X 3 5 XX X X 4 6 X X 2 7 0 8 X X X 2 9 X 1 10 X 1 11 X X X 3 12 0 13 X X 2 14 015 X X 2 16 X X 3 Total 8 6 4 2 3 3 2 1 29

TABLE 3 Distribution of Gene Mutations in Group 2. Tumor ID CDKN2A EGFRPTEN PDGFRA TP53 CDK4 NF1 CDK6 Total 1 X X 2 2 X X 2 3 X X X X X 5 4 X X2 5 X X 2 6 X 1 7 X X 2 8 0 9 0 10 X X 2 11 X X 2 12 0 13 X X 2 14 X X 215 X 1 16 X 1 Total 11 8 3 2 1 0 1 0 25

Variations in Blood Flow and Cellular Density

FIG. 3 demonstrates variation in the normalized values of T1post-gadolinium, T2-weighted, and FLAIR images in different survivalgroups. In the T1 post-gadolinium images, there are two populations thatare roughly Gaussian distributions around high and low means. Thissuggests that GBMs are generally divided into regions of high and lowcontrast enhancement that were viewed as an approximate measure of bloodflow. That is, while the dynamics leading to contrast enhancementincludes blood flow and vascular integrity (extravasation), it wasassumed in the bifurcated classification that the non-enhancing regionshave poorer blood flow than the enhancing regions. Group 1 demonstratesa shift in the distribution of these enhancement regions from high tolow.

The T2-weighted and FLAIR distributions suggest that group 1 tumorsactually contain habitats that are either not present or rare withinlong-term survival tumors. For both FLAIR and T2-weighted histograms,the tumor volume is dominated by a single population, probably with oneother smaller population leading to some asymmetry of the Gaussiandistribution. In group 1, tumor set distribution is significantly moreheterogeneous with at least three distinct regions.

Initial Spatial Analysis

Since the T1 post-gadolinium images were consistently divided into tworegions, this was used as a starting point for combining sequences. Allof the tumors were divided spatially into high and low enhancementregions using a normalized intensity of 0.26 as the dividing point. Thethreshold was found by fitting a Gaussian mixture model [Figueiredo Mand Jain A (2002). IEEE Trans Pattern Anal Mach Intell 24, 3:381-3:396;Banfield J D and Raferty A E (1993). Biometrics 49, 803-821] to acumulative histogram of all T1 post-gadolinium images and finding wheretwo classes intersected. After this spatial division, FLAIR values wereprojected onto the high and low enhancement groups. As shown in FIG. 4,this resulted in clear separation of the GBM images into five distinctradiologically defined combinations of contrast enhancement andinterstitial edema-two with high blood flow and high and low celldensity and three with low blood flow and high, low, and intermediatecell densities. In the high enhancement (i.e., high T1 post-gadolinium)regions, there is a region with low FLAIR signal indicating cell densityand interstitial edema comparable to normal brain tissue. However, asecond habitat with higher FLAIR signal indicates that some tumorregions with high levels of enhancement have lower cell density andhigher interstitial edema than normal tissue. Similarly, in the lowenhancement regions of the tumor, one subregion shows very high FLAIRsignal representing necrosis. However, two additional subregions eachwith less water and more apparent cellularity are also present. Thisindicates the presence of viable cell populations that have adapted tolocal environmental conditions generated by low flow (e.g., hypoxia andacidosis).

Applying Spatial Analysis to Clinical Response

The two groups were analyzed using 3D graphs that plotted the relativefrequency of regions with specific combinations of T1 post-gadoliniumsignal and either FLAIR or T2-weighted signal. As shown in FIG. 4, group2 tumors typically consist of tumor habitats with high enhancement(i.e., >0.26) and relatively high cell density. Group 1 tumors hadincreased regions of low enhancement. Interestingly, while these oftencorresponded to high T2-weighted or FLAIR signal indicating necrosis,regions with low enhancement and relatively high cell density werefrequently present.

Statistical Analysis and Clinical Survival Time Group Prediction

To test the predicative capability, a binary classification scheme(i.e., group 1 and group 2) was formulated. The machine learningclassifier, support vector machines [Chang C C and Lin C J (2011). ACMTrans Intell Syst Technol 2, 1-27; Cortes C and Vapnik V (1995). MachLearn 20, 273-297], was used to classify samples by using a Gaussiankernel function to project features into a high-dimensional space. Bothleave-one-out and 10-fold cross-validation schemes were used forperformance evaluation. The accuracy (81.25% for leave-one-out),specificity, and sensitivity values were determined with resultssummarized in Table 4. In addition, ROC curves are shown in FIG. 6, andthe associated area under the curve values are also given.

TABLE 4 Prediction Performance. Area Under the Cross-Validation AccuracySpecificity Sensitivity Curve Values Leave-one-out 81.25% 77.78% 85.71%0.86 10-fold 78.13% 73.68% 84.62% 0.83

Spatial Mapping

To examine the spatial clustering of habitats, the combined imaging datasets were divided into four arbitrary habitats—high and low blood flowand high or low cell density. These were then projected back onto theMRI studies. In detail, the nonparametric Otsu segmentation approach[Otsu N (1979). IEEE Trans Syst Man Cybern 9, 62-66] was used for theintratumor segmentation. Given a modality, after setting the number ofgroups (two groups in this study) to be segmented, the Otsu algorithmiteratively searched for an optimal decision boundary until convergence.As shown in FIG. 7, habitats generally clustered into spatial groupsafter an intersection operation between two MRI modalities. The choiceof modalities and mapping procedure can be varied according to the needsof the specific task. In short, as a tool for brain tumor heterogeneityanalysis, the design of the spatial habitat concept gave rise to variousopportunities for quantitative measurement (e.g., using these habitatsto quantitatively observe tumor evolution progress).

Discussion

Multiple recent studies have demonstrated marked genetic heterogeneitybetween and within tumors. This is typically ascribed to clonalevolution driven by random mutations. However, genetic mutations simplyrepresent one component (“a mechanism of inheritance”) of Darwiniandynamics, which are ultimately governed by phenotypic heterogeneity andvariations in environmental selection forces [Gatenby R A and Gillies RJ (2008). Nat Rev Cancer 8, 56-61]. While genetic heterogeneity clearlyposes a challenge to molecularly based targeted therapy, thesevariations, rather than a stochastic process governed by randommutations, may represent predictable and reproducible outcomes fromidentifiable Darwinian dynamics.

In the disclosed model, intratumoral evolution is fundamentally governedby variations in environmental selection forces that are largelydependent on local blood flow. That is, while changes in cancer cellsmay be the result of random genetic or epigenetic events, clonalexpansion of each new genotype is entirely dependent on its fitnesswithin the context of the local environment and the fitness of thecompeting tumor populations. Thus, the dominant cancer phenotype andgenotype within each tumor region is largely determined by their abilityto adapt to environmental conditions that are generally governed byblood flow including oxygen, glucose, H+, and serum growth factors. Thissuggests that only limited numbers of general adaptive strategies arenecessary, e.g., evolving the capacity to survive and proliferate inhypoxia. However, the phenotypic expression of those strategies is amuch larger set of possibilities and the genetic pathway to thosephenotypes is likely very much larger [Gatenby R (2012). Nature 49, 55].Thus, the genetic variation among cancer cells could look chaotic evenwhen the underlying evolutionary dynamics are fairly straightforward.

This connection between environmental selection forces and phenotypicadaptations/genetic heterogeneity provides a theoretical bridge betweenradiologic imaging and cellular evolution within tumors. Thus,radiographic manifestation of blood flow and interstitial edema canidentify and map distinctive variations in environmental selectionforces (“habitats”) within each tumor.

To evaluate the potential role of habitat variations in survival, thestudy group was arbitrarily divided into two groups based on survivaltime. The disclosed results demonstrate that group 1 and group 2 GBMshave distinctly different patterns of vascularity and cellular density.As shown in FIG. 4, GBMs consistently divide into five MRI-definedcombinations of blood flow and cellular density. At present, theunderlying evolutionary dynamics cannot be determined unambiguously.Clearly, the expected patterns are high blood flow and high cell densityand low blood flow with low cell density. There are three additionalregions of apparent mismatch between blood flow and cellular density. Ingeneral, it is likely that they represent two possible “ecologies”: 1)cellular evolution. This could result in adaptive strategies that permitincreased proliferation in regions of poor perfusion or increasedutilization of substrate (because of Warburg physiology, for example)that increases glucose uptake and toxic acid production in regions thatare well perfused. 2) Temporal variations in regional perfusion. Thiswould result in cycles of normoxia and hypoxia so that the averageperfusion results in greater or lesser cellularity than expected basedon a single observation.

As shown in FIG. 5, group 2 tumors were more homogeneous with a dominanthabitat in which there is high blood flow and intermediate cell density.Group 1 tumors, however, contain relatively high volumes of low bloodflow habitats that may have very low cell density indicating necrosisbut often exhibit cell densities comparable to those seen inwell-perfused regions.

Multiple factors may be involved including poor perfusion and hypoxia,which may limit the effectiveness of chemotherapy or radiation therapy.Furthermore, hypoxia-adapted cells often exhibit more stem-like behaviorwith up-regulation of survival pathways that confers resistance totreatments.

As disclosed herein, clinical imaging can be used to gain insight intothe evolutionary dynamics within tumors. The disclosed results suggestthat combinations of sequences from standard MRI imaging can definespatially and physiologically distinct regions or habitats within theecology of GBMs and that this may be useful as a patient-specificprognostic biomarker. Ultimately, many other combinations of imagingcharacteristics including other modalities such as FDG PET should beinvestigated and may provide greater information regarding intratumoralevolution. Finally, changes in intratumoral habitats during therapy mayprovide useful information regarding response and the evolution ofadaptive strategies.

Example 2 Soft Tissue Sarcomas (STS)

Introduction

Soft tissue sarcomas (STS) are a heterogeneous group of mesenchymaltissue cancers, with over 50 histological sub-types. Regardless of type,the general course of therapy begins with doxorubicin (DOX) followed byresection, if possible. Response rates to DOX are only 25-40% across allhistological sub-types and remissions are rarely achieved. Due in partto the rarity and diversity of the many histologic sub-types of STS,there have not been any new front-line therapies approved in over 30years. A multi-tyrosine kinase inhibitor, pazopanib, was recentlyapproved for relapsed and refractory STS which increases progressionfree survival by 4 months. Amongst newer agents being developed for STS,TH-302 is showing exceptional promise. TH-302 is an alkylating pro-drugthat is activated only in regions of severe hypoxia, and is currently ina phase III trial in combination with DOX in unresectable STS.

Predicting or assessing response in STS is difficult because traditionalmeasures of response based on tumor size change, such as ResponseEvaluation Criteria in Solid Tumors (RECIST), or those based on relativecontrast enhancement (modified Choi) do not correlate well with overallsurvival (OS) or progression free survival (PFS). A major limitation ofthese methods is that they do not account for the extreme histologicalvariability within individual tumors prior to and following therapy. Infact, heterogeneous responses are commonly observed among differentpatients with the same tumor stage and even among different sub-regionswithin the same tumor. In clinical and pre-clinical STS, distinctintratumoral sub-regions across multiple histological types have beenquantitatively delineated by combining T1, contrast-enhanced T1, and T2STIR MR images. As these are sensitive to tumor physiology, thesesub-regions have been classified as “habitats” within STS.

Because TH-302 is active only in hypoxia, and hypoxia should berepresented by a specific habitat, “hypoxic” habitats can be used topredict and monitor responses to TH-302 across a variety of histologicalsubtypes of STS. In contrast, response to doxorubicin should be limitedto well-perfused portions of the tumor, or “riparian” habitats.Prediction will allow for pre-therapy patient stratification, andmonitoring will allow for adaptive therapy trial designs. Furthermore,habitat imaging across different STS types will generate anunprecedented unifying approach to this heterogeneous group of diseases.

Phenotypic habitats can be identified in the clinic with fuzzyclustering algorithmic combinations of T1, T2, T2 STIR, andcontrast-enhanced (CE) T1 images. Additional MR observable parameters,such as diffusion, can also be used to define habitats with greatersensitivity and granularity. These studies can develop a minimal MR dataset needed to accurately identify relevant habitats in a clinicalsetting. Images are grossly co-registered to histology andimmunohistochemistry (IHC) to identify underlying molecular biochemistryand morphology of individual habitats.

Xenotransplanted tumors can be treated with standard and adaptive dosingschedules of TH-302±doxorubicin. Responses can be quantified as overallsurvival (OS) and tumor control as defined by the Pediatric PreclinicalTesting Program (PPTP). Responses can be compared to pre-therapy habitatimages, as well as pre- and post-therapy changes in habitats. Studiescan begin with standard dosing, and progress to adaptive dosing informedby changes in habitat volumes.

Effectors can be tested for their ability to increase oxygen consumptionin a panel of STS cell lines and STS tumor tissue in vitro. Those withgreatest response can be tested in vivo for their ability to acutelyenhance tumor hypoxia, and the effects of these agents can be tested fortumor response in combination with TH-302 using STS Xenotransplants.

A clinically viable MR-exam is disclosed that can be used to quantifyextent of defined habitats, whether these habitats can be used topredict or monitor therapy response, and whether responses can beimproved by manipulating habitats.

Soft Tissue Sarcomas (STS) are a heterogenous group of mesenchymaltumors with >50 histological subtypes. STS are considered rare, with anincidence of 13,170 new cases per year in the U.S. Although they areonly 1% of adult malignancies, sarcomas make up 15% of all cancers inpatients under the age of 20 [Burningham Z, et al. Clin Sarcoma Res.2012 2(1):14]. Moffitt is a referral Cancer Center and treats >400 newSTS cases every year. Despite the heterogeneity, STS are commonlytreated the same, e.g. with doxorubicin (DOX), which delivers a responserate of 25-40%, followed by surgery and radiation, if possible. Mediansurvival can be as long as 10 years in these cases. However, for thosethat are refractory or recur, median survival is a dismal 18 months.Hence there is a great need for improved therapeutic options. Yet,because of the heterogeneity and rarity of STS, no new front linetherapies have been developed to treat STS for decades.

A number of novel chemotherapies for STS are on the horizon. There arecurrently 13 active phase III interventional drug trials. Most of theseare biomarker driven trials using existing agents, yet some arepromoting the application of novel chemotherapies. Ifosfamides (e.g.palifosfamide) are DNA-alkylating agents that are increasingly beingused in STS in combination with DOX. An exciting advance has come withTH-302, which is a prodrug that has an ifosforamide “warhead” that isreleased only in regions of profound hypoxia, i.e with pO2<10 mm Hg [LiuQ, et al. Cancer Chemother Pharmacol. 2012 69(6):1487-98; Meng F, et al.Mol Cancer Ther. 2012 11(3):740-51; Sun J D, et al. Clin Cancer Res.2012 18(3):758-70). TH-302 is currently in 11 active or planned clinicaltrials, including a phase III in non-resectable STS (Table 5).

TABLE 5 Clinical Trials with TH-302 NCT00495144 Advanced Solid Tumors CPhase 1/2 NCT00742963 +/−Doxorubicin in Advanced Soft Tissue Sarcoma APhase 1/2 NCT01440088 +/−Doxorubicin in Unresectable or Metastatic SoftTissue R Phase 3 Sarcoma NCT01746979 +Gemcitabine in UntreatedUnresectable Pancreatic R Phase 3 Adenocarcinoma NCT01522872+/−Bortezomib in Relapsed/Refractory Multiple Myeloma R Phase 2NCT01864538 A Phase 2 Biomarker - Enriched Study in Advanced Melanoma NPhase 2 NCT01381822 +/−Sunitinib in RCC, GIST and pancreaticNeuroendocrine R Phase 1/2 Tumors NCT01497444 +Sorafenib in unresectableKidney or Liver Cancer R Phase 1/2 NCT01833546 in Solid Tumors andPancreatic Cancer (Japan) R Phase 1 NCT01149915 Advanced Leukemias RPhase 1 NCT01721941 +Dox by trans-arterial chemoembolization in HCC NPhase 1 NCT01485042 Pazopanib Plus TH-302 (PATH) advanced solid tumors RPhase 1 R = recruiting; N = not yet recruiting; C = completed; A =Active no longer recruiting

Moffitt Cancer Center is participating in NCT 01440088, which comparesTH-302+DOX to DOX alone, with a primary endpoint of OS. DOX (75 mg/m²)is administered on day 1 of a 21-day cycle, for up to 6 cycles. TH-302(300 mg/m²) is infused on days 1 and 8, and may continue indefinitely ona maintenance basis, as it is well-tolerated, based on phase I data. Asecondary endpoint is an objective response measured by RECIST 1.1.

A major limitation in the use of RECIST as an objective endpoint is thatmany diseases (notably STS) and many therapies do not result inobservable changes in tumor size. Hence, specificity can be high, butsensitivity of RECIST is poor to predict survival in STS [Schuetze S M,et al. The oncologist. 2008 13 Suppl 2:32-40; Stacchiotti S, et al.Radiology. 2009 251(2):447-56; Benjamin R S, et al. J Clin Oncol. 200725(13):1760-4]. Consequently there is a need to exploit more modernimaging approaches to predict and evaluate response of STS tochemotherapy, whether in a neo-adjuvant or salvage setting. One of thereasons that anatomic size-based methods are deficient is that theytreat tumors as homogeneous and well-mixed, which they are not.

Habitat Imaging.

It is well known that malignant tumors are highly heterogeneous in theirphysical microenvironments, genetics and molecular expression patterns[Gerlinger M, et al. The New England journal of medicine. 2012366(10):883-92]. Tumors can be described as complete ecosystems,containing cancer cells, stromal cells, vasculature, structuralproteins, signaling proteins and physical factors such as pH and oxygen[Gillies R J, et al. Cancer metastasis reviews. 2007 26(2):311-7]. Infact, tumors have been described as “continents”, which contain multipledomains with distinct microenvironments. Because these micro-domains, or“habitats”, contain unique mixtures of cells, physical and biochemicalcharacteristics, they will have differential responses to therapies anddifferential evolutionary trajectories [Gillies R J, et al. Naturereviews Cancer. 2012 12(7):487-93]. Knowledge of these habitats canpotentially provide patient benefit by stratifying therapeutic choices,and identifying inactive approaches early in the course of therapy.

Magnetic resonance imaging (MRI), which is commonly used to characterizeSTS, can quantitatively identify distinct habitats within STS, and thatthese data can be used to monitor and predict therapy response. Suchknowledge could have profound impact on the treatment of STS patients inthat it will improve decision support systems for choice of therapy andtherapy course. Such measures could be made early and often, to assesswhether a particular therapeutic treatment is working or not. Accordingto NCCN guidelines, MRI is a preferred imaging modality for STS becauseof its excellent soft tissue contrast and higher diagnostic potential,compared to computed tomography (CT). Strategies that leverage clinicalstandard-of-care (SOC) images, with post-processing methods to separatedistinct habitats, could be readily implemented into clinical trials andto clinical practice. SOC MRI pulse-sequences commonly include apre-contrast T1; a T2 STIR; sometimes followed by a diffusion sequence,followed by a contrast-enhanced T1. Each of these provides informationrelevant to delineation of distinct physiological habitats, as shown inTable 6 [Shinagare A B, et al. AJR Am J Roentgenol. 2012 199(6):1193-8].Combining these orthogonal pieces of information can better identifydistinct habitats; and refining these MR pulse sequences may furtherimprove the ability to identify distinct and therapeutically relevanthabitats.

TABLE 6 Information gained from common MR pulse sequences of STS ImageParameter Map T2 FSE Fast Spin Echo = Borders, edema T2 STIR Short T1Inversion Recovery = Fat suppressed (processed) Subtracting STIR fromFSE = Lipid distribution T1 Baseline T1 CE Contrast Enhanced = Areas ofagent extravasation (processed) Low CE = Poorly perfused regions GRASEGradient and Spin Echo = vascular conspicuity Diffusion QuantitativeEdema (inverse is cellularity)

Disclosed herein is the interpretation of standard-of-care images usingecology and evolutionary dynamics. Combinations of MR images thatcontain orthogonal information can be used to identify the distincthabitats that together make up a tumor.

The vasculature of tumors is known to be chaotic: it is tortuous,imbalanced, of low tone, and hyperpermeable. These traits are regionallyvariable, leading to spatial and temporal variations of oxygen andnutrients. Adaptation of cells to these different perfusion habitats canhave profound effects on their molecular expression patterns, theiraggressiveness and their therapy responsiveness. Regional differences inoxygenation play an important role in tumor evolution, especially inregions that are intermittently oxygenated. Adaptation to hypoxiainvolves significant metabolic reprogramming as well as a selection forcells that are apoptosis-deficient [Tatum J L, et al. Int J Radiat Biol.2006 82(10):699-757].

Disclosed are clinically feasible imaging approaches to quantitativeidentify habitats in soft tissue sarcomas. Although the current examplefocuses on “hypoxic” and “riparian” (well-perfused) habitats, otherhabitats with other distinct cellular behaviors are identifiable bythese methods. Hypoxia was chosen because of its relevance to tumorbiology, the supposition that hypoxic habitats will be readilyidentifiable, and the availability of a hypoxia-activated prodrug, HAP,that is in clinical trials. The HAP approach itself is highly innovativein that it targets a cancer phenotype rather than genotype, whichincreases its likelihood of managing STS across all histological types.

The disclosed methods also provide a common biomarker platform that willbe applicable across all soft tissue sarcomas, regardless of theirhistology or molecular expression patterns. In a 2×2 clustering system,the same 4 habitats have been observed with differing amounts across 5types of STS in patients. Hence the habitat imaging approach can lead tothe development of a new lexicon that can be used across all STS tumorsto provide improved decision support.

The general approach in this example involves 1) acquiring multipleco-registered MR images; 2) using the results from these scans to buildup a data cube for each voxel in the image; 3) grouping voxels togetherthat have similar patterns to visualize habitats; 4) in parallel,extracting habitat information from whole mount histology and IHC; and5) iteratively comparing MR and histology to generate a more precisedescriptions of the habitats. This general approach can be applied tohuman-in-mice xenotransplant sarcoma tumors, and to cultured sarcomacells and xenografts. A pre-clinical trial can also be conducted,wherein the image-defined hypoxic habitat is used to predict and monitorthe response to doxorubicin and the hypoxia activated pro-drug, TH-302.This can be used to develop biomarker-driven trials going forward.

Results

Differentially Define MRI-Visible Habitats in STS

Whole-mount histology of the osteosarcoma xenotransplant is shown inFIG. 8A, and these H&E data were analyzed using quantitativehistopathology to identify clusters of cells with similar combinationsof morphological features, shown in FIG. 8B. This was achieved usingDefiniens Developer XD and Tissue Studio platforms to automaticallyidentify all nuclei (up to 50,000 per field), and cell boundaries, asdescribed above and in [Lloyd M C, et al. J Pathol Inform. 2010 1:29]. Areport is then generated that extracts 32 features from each cell. Inthis example, cells were clustered according to their nuclear area,Haralick texture of nucleus and intensity of eosin staining into fivesub-regions with similar morphotypes. There are two importantconsideration from these analyses: (1) While some of these clusters arevery small, there are at least 3 clusters that are >2 mm and thusvisible by MRI (cf. FIG. 9). Thus, 1 mm, was the target precision forregistering MR with histology. Further, (2) IHC images can be used toidentify the molecular phenotypes within these habitats. FIG. 9 shows arepresentative set of spin echo T2 and diffusion images from anosteosarcoma xenotransplant. In the T2 image, note that the tumorsexhibit a significant amount of regional heterogeneity. The diffusionmaps were calculated to display the Apparent Diffusion Coefficient, witha large dynamic range. High diffusion (>3 u² sec⁻¹=red) is associatedwith edema, which is commonly found in necrotic volumes. Low diffusion(˜1.2 u² sec⁻¹=dark blue) is associated with high cell density. Notably,volumes with high cell density will have a higher oxygen consumptionrate and be more prone to hypoxia. Thus, we propose that areas of lowdiffusion that are adjacent to areas of high diffusion are hypoxic. Suchvolumes can be visualized by plotting and analyzing ADC gradient maps.

To further examine possibilities of imaging hypoxia, imaging can be donewith diffusion and dynamic contrast enhanced (DCE) MRI. The rationalefor this approach is shown in Table 7. Volumes with lowest signal oncontrast enhanced (CE) T1 are in some embodiments poorly perfused.Within these volumes, low cell density would be found in necrotic areas,whereas, in contrast, high cell densities would have highest oxygenconsumption and thus be more prone to hypoxia. Additionally, barringbiochemical chemo-resistance mechanisms, those volumes that are wellperfused with high cell density areas would experience higher drugconcentrations and thus be most responsive to, e.g. doxorubicin,chemotherapy. Diffusion (DW) MRI is a quantitative measure of edema andnecrosis [Jordan B, et al. Neoplasia. 2005 7(5):475-85; Norris D. NMRBiomed. 2001 14(2):77-93], and necrotic volumes are commonly observedadjacent to hypoxia. FIG. 10 shows T2 images of (A) a pancreatic cancerpatient and (B) an orthotopic MiaPaCa-2 pancreatic onto which wereidentified those pixels that had the lowest quartile in ContrastEnhanced T1 images and either the highest quartile ADC values(green=necrosis) or the lowest quartile (purple=“hypoxia”). These 3D MRimages can be compared to 2D histology, or immunohistochemistry ofbiological and tracer hypoxia markers.

TABLE 7 MRI visible habitats Diffusion (ADC) Lowest ¼ Highest ¼ (highcell density) (edema) CE T1 Lowest Quartile HYPOXIC NECROTIC (poorlyperfused) (purple) (green) Highest Quartile RIPARIAN Not observed (wellperfused) (grey)

In a pilot study, clinical T1-weighted pre-contrast, T1-post contrastand T2-STIR were analyzed, where the inversion delay Ti is set to nullout the signal from fat, and the results are shown in FIG. 11. As shownin FIG. 11, the orthogonal data can be combined using Fuzzy C-means andOtsu segmentation clustering to identify spatially explicit habitats inSTS. This has been performed in 5 different histological subtypes, allof whom show the same collection of habitats, albeit in differentproportions.

Relate Habitats to Therapy Response

FIG. 12 shows the response of HT1080 Sarcoma tumors in SCID micefollowing treatment with vehicle, TH-302, doxorubicin and(TH-302+doxorubicin). As shown, these two agents combine for maximumcell kill. Notably, this synergy is only observed if TH-302 is givenprior to doxorubicin. This is interpreted as evidence that doxorubicinwill exert its effects to the cells closest to patent vasculature andthat TH-302 exerts its effects on cells in poorly perfused regions (seeTable 7). If doxorubicin is effective in killing cells adjacent to thevasculature, it will result in expanding the effective diffusiondistance of 02 due to lack of consumption by intervening cells, thusexpanding the well-oxygenated volume and reducing efficacy of TH-302.

In a parallel study, DCE-MRI and Diffusion MRI were investigated asresponse biomarkers to TH-302 in flank implanted MiaPaCa2 pancreaticadenocarcinoma (PDAC) models. These studied showed a strong correlationbetween initial drop in K_(trans) and tumor control, with no differencesin ADC at any time point [Cardenas-Rodriguez J, et al. Magn ResonImaging. 2012 30(7):1002-9]. In a follow-on study, orthotopicallyimplanted Hs766t, MiaPaCa2 and Su.86.86 PDAC cells were used, which havedifferential sensitivities to TH-302 as monotherapy. These studiesshowed decreases in Ktrans in the responsive Hs766t and MiaPaCa2, butnot the resistant Su.86.86 in response to TH-302. Notably, no changes inthe mean ADC were observed, and these studies underscore the need forimproved imaging response biomarkers for TH-302 therapy.

Combined MRI and histology image analyses were used to examine effectsof dasatinib+Tcn in a Ewings sarcoma model; and the response to a novelWee-1 checkpoint inhibitor, MK1775, alone and in combination withgemcitabine in osteosarcoma xenotransplants [Kreahling J M, et al. MolCancer Ther. 2012 11(1):174-82; Kreahling J M, et al. PloS one. 20138(3):e57523]. In the Ewing's model, the pre-therapy spin-echo T2 andpost-therapy response of ADC were compared to tumor volume changes.These analyses showed that the change in ADC within 24 hr followingtherapy was a powerful predictor of eventual tumor volume changefollowing therapy. Notably, as mentioned above, TH-302 does not appearto invoke a change in ADC to presage response.

In the osteosarcoma xenotransplant models, the change in ADC wasmeasured following treatment with gemcitabine and the wee-1 inhibitorMK-1775, alone and in combination. As shown in FIG. 13, the ADChistograms shifted to the right within 48 hr following gemcitabine andcombination therapy groups, which had a robust anti-tumor response; butnot control or MK-1775 mono-therapy groups, which were non-responsive.The change in ADC was highly correlated with tumor growth rate response.Also, the shapes of the histograms changed with response, going fromGaussian to a log-normal distributions, with increased skewness.

At a deeper level of analysis, the ADC maps were analyzed for imageentropy, which is calculated as the randomness of a 3×3 pixel matrixsurrounding the pixel under test (P-U-T). Maps of ADC-Entropy for eachtreatment group are shown in FIG. 14A, and show that all tumors haveentropically definable “habitats”. FIG. 14B shows that there weresignificant differences in the habitat distributions between tumors thatresponded to therapy (Gem and Combo) compared to those that did not(Ctrl and MK1775).

Example 3 Prostate Cancer

Methods

Active Surveillance:

Prostate cancer is often over-treated, as many men will not becomesymptomatic or die from their disease. As has been described by theUrology group at the University of Miami, active surveillance (AS) is anattractive alternative to primary therapy with total prostatectomy orradiotherapy, buying time to determine if the disease needs to betreated and preserving function in many patients for over 5 years[Soloway M S, et al. European urology. 2010 58(6):831-835; Klotz L. Pro.J Urol. 2009 182(6):2565-2566; Soloway M S, et al. BJU Int. 2008101(2):165-169]. The postponement of treatment and preservation ofquality of life is of primary importance, particularly for men in their50's and 60's. Since men in these age ranges most often have a long lifeexpectancy, it is imperative that the window of opportunity for cure bepreserved.

Older surveillance studies in the pre-PSA era have documented thenatural progression of prostate cancer, illustrating the metastasis andmortality risks. Death due to prostate cancer occurs late, but issignificantly greater in men observed versus those treated primarily[Bill-Axelson A, et al. J Natl Cancer Inst. 2008 100(16):1144-1154]. Thekey is to determine early those who are not good candidates for activesurveillance. Recent reports show a 22-30% rate of conversion totreatment by 2-3 years. The men who require early conversion areprobably those who have been understaged and/or undergraded byconventional assessments.

MP-MRI of the Prostate:

T2w MRI provides an excellent depiction of prostate anatomy with lowersignal intensity in prostate cancer [Hegde J V, et al. J Magn ResonImaging. 2013 37(5):1035-1054]. Diffusion Weighted Imaging (DWI) issensitive to water molecule diffusion and the derived Apparent DiffusionCoefficient (ADC) values are significantly lower in tumor than in normalprostate due to restricted water diffusion. The lower the ADC value, thegreater the chance of diagnosing Gleason score (GS) 7 disease [Vargas HA, et al. Radiology. 2011 259(3):775-784 Somford D M, et al. InvestRadiol. 2013 48(3):152-157; Peng Y, et al. Radiology. 2013267(3):787-796]. Dynamic contrast enhanced (DCE)-MRI has also beenapplied to discriminate normal from malignant prostate tissues, withearlier and greater enhancement followed by washout seen in the latter.DCE-MRI measures vascularity and hence angiogenesis. Both DWI and DCEhave a relatively high sensitivity and specificity for prostate cancer[Vargas H A, et al. Radiology. 2011 259(3):775-784 Mazaheri Y, et al.Radiology. 2008 246(2):480-488; Schmuecking M, et al. Int J Radiat Biol.2009 85(9):814-824; Isebaert S, et al. J Magn Reson Imaging. 201337(6):1392-1401]. MP-MRI that includes T2, T1 non-contrast, DCE-MRI, andDWI sequences results in higher sensitivity, specificity and accuracy oftumor localization [Isebaert S, et al. J Magn Reson Imaging. 201337(6):1392-1401; Sciarra A, et al. Eur Urol. 2011 59(6):962-977].

Transrectal Ultrasound (TRUS) and MRI Targeted Biopsies in ActiveSurveillance Patients:

Prostate cancer is often multifocal and heterogeneous and thus presentsa challenge in identifying biopsy sites. Transrectal Ultrasound (TRUS)guided biopsy remains an imprecise technique with 30% or more ofprostate tumors being isoechoic [Shinohara K, et al. J Urol. 1989142(1):76-82] and a roughly 50:50 chance of documenting cancer inhypoechoic lesions [Gosselaar C, et al. BJU Int. 2008 101(6):685-690].Since the biopsy needle cannot be directed reliably to a tumor focus, agrid-like systematic biopsy of the gland is now routine. Highest gradeand/or volume lesions are often missed. MRI-guided prostate biopsies viaMRI-ultrasound (MRIus) fusion or biopsies done directly on an MRI yielda higher proportion of positive biopsies, especially from regions of lowT2 intensity, low ADC values and high early contrast enhancement.

Radiomics:

Radiomics data are in a format that is amicable for building descriptiveand predictive models relating image features to outcome, as well asgene-protein signatures. Resultant models may include imaging,molecular, and clinical data, and provide valuable diagnostic,prognostic or predictive information. Image features has been used byothers to relate MRI or CT image features to global gene expressionpatterns in glioblastoma multiforme (GBM) and hepatocellular carcinoma(HCC) [Diehn M, et al. Proc Natl Acad Sci USA. 2008 105(13):5213-5218;Segal E, et al. Nat Biotechnol. 2007 25(6):675-680]. Image texturefeatures in CT of the lung that are prognostic of survival have beendeveloped [Basu S, et al. 2011 1eee International Conference on Systems,Man, and Cybernetics (Smc). 2011:1306-1312]. These methods aresemi-automated wherein the radiologist identifies the lesion andcomputer software proceeds to segment, render and generate a report ofquantitative features. These reports are pertinent to the questions:Which features are informative (e.g. have a wide range and aremeasurable in all samples)? What is the variance from one measurement toanother and what are the critical sources of that variance? Are thefeatures with largest dynamic range related to outcomes?

As disclosed herein, specific “habitats’ from radiological images can beused with radiomics for prostate cancer analysis [Gatenby R A, et al.Radiology. 2013 269(1):8-15]. This approach involves the combination ofco-registered images from multiple modalities, with each onecontributing a piece of orthogonal information. For this reason, MRI isa technique of choice because multiple pieces of co-registeredorthogonal data can be generated in a single exam. For example, DCE-MRIis a powerful method to identify regional distributions of blood flow,and lack of blood flow. The texture of these enhancements have proven tohave significantly higher prognostic value than simpleregion-of-interest (ROI) measures. Diffusion MRI measured ADCs is apowerful method to interpolate the density of diffusion barriers (i.e.cells) and hence provides information that may be biologically, but notphysically, related to DCE. T2 is sensitive to microscopic perturbationsin the magnetic field; this is affected by blood flow and cell density,but in a non-linear fashion. Hence, T2 information is not strictlyorthogonal to DCE and ADC and this correlation is accommodated inhabitat imaging.

Results

MP-MRI and Active Surveillance.

In some cases, the practice is to classify suspicious imaging tumorvolume regions (SImTVs) as dominant or nondominant usingsemi-quantitative methods, and describe tumor location and extent fromthe intersection of three spatial maps: (i) DCE-MRI based on tumorpattern weight W_(T)>mean(W_(T))+stdev(W_(T)) and (ii) DWI determined byADC<1,000 μm²/s, and (iii) T2w intensity. Dominant SImTV(s) will have ahypointense T2w lesion corresponding with the highest W_(T) and lowestADC values. Any other combination with pathologic confirmation would beconsidered nondominant. Persistence of disease in the dominant lesion isa common mechanism responsible for progression [Pucar D, et al. Int JRadiat Oncol Biol Phys. 2007 69(1):62-69]. Suspicious dominant andnondominant tumor volumes will be targeted for MRI-directed biopsies.

The integrated platform for SImTV visualization and analysis uses MIMSoftware Inc (Cleveland, Ohio, USA). Using MIM extensions, anunsupervised pattern recognition technique is implemented foridentification and automatic delineation of SImTVs in the DCE-MRI (FIG.18). The approach is based on non-Negative Matrix Factorization (NMF),described in detail in Stoyanova et al [Stoyanova R, et al. TranslOncol. 2012 5(6):437-447]. Briefly, the DCE-MRI data matrix D(containing the individual pixel contrast-to-time curves in rows) isrepresented as a product of k basic contrast signatures S(t) and theirweights W(X), i.e. D˜W×S under the constraint that all elements of W andS are non-negative. The tumor area is characterized with rapid uptakefollowed by continuous washout of the contrast (FIG. 19C). The weightsW_(T), corresponding to this contrast-to-time pattern represent theDCE-tumor map and after applying threshold=mean(W_(T))+stdev(W_(T)), atumor volume is created (FIG. 19E, red). Here and in the rest of thetext we will refer to W_(T) as DCE_(T). Low ADC values (e.g., <1000s/mm² has been used, but this is selectable) are also auto-contoured inMIM (FIG. 19E, red-green overlap is yellow).

Using this procedure, 65 consecutive patients (pts) were analyzed andSImTVs identified in 83% (n=54) [Stoyanova R, et al. InternationalJournal of Radiation Oncology Biology Physics. 2011 81(2):5698-5699].The SImTVs were 0.3 to 11.7 cc (mean±SD: 2.9±2.7 cc and median=1.85 cc).Prostate volumes were 40.1±20.9 cc (median=34.7, range=14 to 111.2 cc).Peripheral zone (PZ) volumes were 10.1±4.8 cc (median 9.3, range 2.5 to27.6 cc. SImTVs were on average 8% and 30% (median 6% and 22%) of theprostate and PZ volumes. SImTVs were detected in the PZ, central gland(CG; also called transition zone or TZ) or both in 34 (63%), 11 (20%)and 9 (17%) of pts, respectively. ADC maps improve the differentiationof malignant from benign tissue, especially in the TZ [Jung S I, et al.Radiology. 2013], in which associated inflammation is a confoundingfactor on DCE-MRI. ADC maps (FIG. 19), in combination with T2w intensitycompliment DCE and improve TZ tumor localization [Jung S I, et al.Radiology. 2013].

MRI-Ultrasound (MRIus) Fusion for Targeted Prostate Biopsies:

MP-MRI—ultrasound image fusion (MRIus) in the Artemis™ system (Eigen,CA) improves the rate of positive biopsies [Abazov V M, et al. Physicalreview letters. 2011 107(12):121802; Sonn G A, et al. J Urol. 2013189(1):86-91]. A 3D TRUS is acquired just prior to biopsy byreconstructing sweeps of 2D to 3D. Both of these volumes are subject toa semiautomatic segmentation [Li B S, et al. 2002 47(3):439-446] thatinvolves the specification of four or more points along the glandboundary (FIG. 20). The original 3D TRUS volume and warped MRI volumeare both readjusted to correspond to the real time 2D ultrasound image.FIG. 21 illustrates a case in which an anterior lesion was identified ina patient being considered for active surveillance. The MRIus biopsyconfirmed high volume GS 7 disease. Subsequently, the patient receivedradiation treatment.

MP-MRI and MP-MRI-guided prostate biopsies have identified a number ofactive surveillance (AS) patients with anterior or lateralized lesionsthat were contained Gleason grade 4-5 disease, who were originallythought to be ideal candidates for AS, and our findings are inconcordance with others who have used these approaches [Stamatakis L, etal. Cancer. 2013 119(18):3359-3366; Hoeks C M, et al. Invest Radiol.2013].

Methods

MP-MRI Habitats in the Prostate.

The MP-MRI prostate exam is composed of three modalities which representanatomy, cellularity and blood flow. Tumor is typically identified by atrained radiologist, looking at each modality and contrast phasesmanually. Other imaging regions, however, are not identified in thisanalysis. The contiguous regions across a modality based on a givencriterion are referred to as habitats. A computational method isproposed to connect three modalities (T2, DWI and DCE) to find regionsof interest that will characterize the prostate beyond the tumorlocation. The habitats are identified separately in PZ and Central Gland(CG) because of different imaging characteristics of the prostate zones(FIG. 22).

Let I_(T2), I_(ADC) and I_(DCE) be the image in R² space for the T2, ADCand DCE_(T), resp. For simplicity, the approach is described for asingle slice with square FOV (n×n). The three modalities are registeredwith MIM Software which provides high level of accuracy of alignment.Two Regions of Interest (ROIs): Prostate and Peripheral Zone (PZ) aremanually contoured in MIM. The pixels within the prostate are classifiedin three groups based on the distribution of the pixel intensity. Thereare optimal methods for distributional delineation; the most popular tofind an optimal cutoff is the Otsu threshold method [Sezgin M, et al.Journal of Electronic Imaging. 2004 13(1):146-168]. In this approach,the subpopulation obtained by the categorization is called high, low anduncertain with respect to the pixel intensities. The pixels of interestacross modalities are the ones that correspond to the common region ofinterest, which can be written as the intersection of the regions.

Ψ=I _(T) ₂ (i,j)∩I _(ADC)(i,j)∩I _(DCE) _(T) (i,j),  (e.1)

where the intensities in each modality satisfies the followingconditions:

I _(T2)(i,j)≦θ_(T) ₂ ;I _(ADC)(i,j)≦θ_(ADC) ;I _(DCE) _(T) (i,j)≧θ_(DCE);i,j=1, . . . ,n.

θ_(T2), θ_(ADC) and θ_(DCE) are the cutoffs for high or low habitats inT₂, ADC and DCE_(T) image of size n×n. The same conditions could betranslated to the entire tumor by taking all the slices for the regionof interest. FIG. 23 shows the distribution of pixels in the samplecase, with vertical lines corresponding to possible cutoffs. An exampleof habitats are shown in FIG. 24. In this patient there was no overlapin CG based on the three criteria (Eq. 1). Prostate biopsies are placedin reference to the dominant and nondominant SImTVs identified. Thebiopsy histopathologic results (i.e., percent core tumor tissue, GLSCand percent Gleason grade 4) will then inform on the relevance of thehabitat(s).

Radiomics of Prostate Habitats.

Radiomic image features are extracted from each of the identifiedhabitats. These features describe characteristics of the image intensityhistograms (e.g., high or low intensity), tumor shape (e.g. round orspiculated), texture patterns (e.g. homogeneous or heterogeneous), aswell as tumor location (PZ, CG). Several types of image features can beextracted to describe the tumor's heterogeneous shape and structure.Note there are multiple features extracted in some of the categories.Texture features have been shown to be good descriptors of the tumor andhave shown relevance for survival prediction [Basu S, et al. IEEESystems Man and Cybernetics. Anchorage: IEEE Systems Man andCybernetics. 2011]. The list of the features (Table 8) is expanded basedon 219 custom 3D image features developed for lung CTs [Basu S, et al.2011 Ieee International Conference on Systems, Man, and Cybernetics(Smc). 2011:1306-1312]. Using the contoured volumes for the prostate andPZ, as well as the identified tumor and other habitat(s), the volumedescriptors are calculated (C1, TABLE 9). Quantitative featuresdescribing the geometric shape of the tumor can also be extracted fromthe 3D surface of the rendered volumes (C2, Table 8). For example, thesurface-to-volume ratio can be determined, where a tumor with irregularform (FIG. 24) has a higher value than a round tumor with a similarvolume. Category C3 describes tumor location (PZ, CG and PZ/CG).Intensity histogram based features (C4, Table 8) reduce the 3D data intoa single histogram and common statistics for each region/modality can becalculated (e.g. mean, median, min, max, range, skewness, kurtosis).Co-occurrence matrix features are widely used for texture classification(C5, Table 8). The joint conditional probability density functionP(i,j;a,d) is in the basis of co-occurrence matrix: the elements (i,j)represent the number of times that intensity levels i and j occur in twovoxels separated by the distance d in direction a. Subsequently, fromthis conditional probability density function, features can beextracted, e.g., describing autocorrelation, contrast, correlation,cluster prominence, cluster shade, cluster tendency, dissimilarity,energy, homogeneity, maximum probability, sum of squares, sum average,sum variance, sum entropy or difference entropy, etc. Further, graylevel run length features are extracted. A gray level run is the length,in number of pixels, of consecutive pixels that have the same gray levelvalue. Most size and shape based feature computations will beimplemented within the Definiens XD® platform (Munich, Germany), whiletexture and other derived features are computed from algorithmsimplemented in C/C++.

TABLE 8 Broad radiomics feature categories Cat. Name DescriptionModality PZ CG Tumor Habitat Total C1 Region Volume Descriptors T2w X XX X 5 Size C2 Region Roundness/irregular T2w X X 24 Shape descriptorsCE-T1 X X 24 ADC X X 24 C3 Tumor PZ, CG T2w X X X X 6 Location C4 PixelHistogram of Intensity T2w X X X X 24 Intensity ADC X X X X 24 DCE_(T) XX X X 24 C5 Grayscale: Run length and Co- T2w X X 34 occurrence patternsADC X X 34 DCE_(T) X X 34 C6 Wavelets Wavelet kernels (entropy T2w X X60 and energy) ADC X X 60 DCE_(T) X X 60 Total 432

Reproducibility of Image Features:

As expected, the radiomics approach generates hundreds of variables,some of which may be redundant. In addition, the numbers of extractedfeatures can be higher than the number of samples in the study, reducingpower and increasing the probability of overfitting the data. In theprior CT study, a combination of ad hoc methods for dimensionalityreduction was used for reproducibility. In this proposal, we will applya similar approach for selection of features, that are highlyreproducible, informative and non-redundant. First, using test-retestprostate image dataset, highly reproducible features will be selectedbased on the concordance correlation coefficient, CCC, with a cutoff of0.90 for high reproducibility. Subsequently, the CCC-prioritizedfeatures will be analyzed for dynamic range, calculated as the ratio ofscalar biological range to the test-retest absolute difference. Featuresshowing high dynamic range are considered to be informative. A dynamicrange of can be arbitrarily used as a cutoff, although features withlower dynamic range may also be informative. Finally, the redundancy inthe features, selected after passing through reproducibility and dynamicrange requirements, can be reduced by identifying highly correlatedfeatures based linear dependency measured by coefficient ofdetermination (R²) across all samples. An R²>0.95 is considered to behighly redundant and thus can be combined into a single descriptor. Inthe lung CT set, the serial application of these three methods reduced aset of 219 features to 48 features that were reproducible, informativeand not redundant (R²<0.95, CCC≧0.90).

Statistical Analysis:

To perform feature selection of high-throughput MP-MRI radiomics inhabitats associated with adverse histologic parameters and biofluidmarkers PSA, PCA3 and pro-PSA. A two-stage feature selection approachcan be used. In the first step radiomics variables can be filtered outusing standard univariate analysis (t-test or ANOVA) and in the secondstep tree based classification (like random forests) can be performed toidentify radiomics signatures of habitats in relation to risk groups.The predictive performance of radiomics signature can be evaluated usingmisclassification error rate of out-of-bag (MER_(OOB)). Random forestsmethodology can be used to identify radiomics features correlated toPSA, PCA3, and pro-PSA.

Digital Histology.

The histology pattern recognition technology suite Tissue Studio v3.0(Definiens, Munich, Germany) can be used as a customized tool toidentify tumor regions of interest and segment cancer from thesurrounding tissues before segmenting single cells within the tumorregions for each digital slide. In H&E cells will be analyzed forevidence of distinct morphologies. This is accomplished by analyzing the34 morphological features that can be discriminated by Otsu thresholdingas “low” or “high”, and these are combined into 2×2, 3×3 or 4×4 matrixesto define 4, 9 or 16 distinct “morphotypes” wherein cells within eachare expressing similar combinations [Lloyd M C, et al. J Pathol Inform.2010 1:29]. Morphotypes generally cluster into spatially distinctregions (“microhabitats”). Stroma often, but not always, presents with acommon morphotype, whereas the cancer cells commonly exhibit 4 or 5distinct clusters. FIG. 25 is an example of two Gleason grade 6 corebiopsies that have different levels of stromal involvement, which can beprognostically important [Basanta D, et al. British journal of cancer.2012 106(1):174-181]. For IHC, each cell can be classified by softwarebased on the biomarker intensity into quintiles, representing from 0 to4+staining. Heatmaps of the resulting expression patterns can furtheridentify clusters or microhabitats, that are related to the H&E data.

Predictive Feature Modeling.

In this study advanced imaging (Radiomic, R), histological (DigitalPathology, P) and clinical (Genomic markers, other clinical parameterslike PSA, Gleason score etc, C) could be used in the predictive model tofind a therapeutic score r, which is a real value (could also bebinarized into Yes/No with a unbiased midpoint cutoff), which is writtenas,

τ=f(Radiomic,Pathology,Clinical)

The function could be linear or non-linear based on the sample size andfeature set. In linear settings, say n₁, n₂, n₃ are subset ofinformative features derived in Radiomics, Pathology and Clinicalrespectively, the above functional could be written as:

τ=Σ_(i=1) ^(n) ¹ α_(i) R _(i)+Σ_(i=1) ^(n) ² β_(i) P _(i)+Σ_(i=1) ^(n) ³γ_(i) C _(i)

Where α, β and γ are optimal weights. The informative features in eachcategory would be obtained by finding best predictive feature in thetraining set. Using the High performance computing best n-dimensionalfeature can be obtained (limited by the computing power, unfetteredaccess to Moffitt 696 core HPC). Conventional support vector, naïveBayes, K-NN type class prediction methods could also be attempted tocontrast the estimates. In order to make unbiased inference, syntheticsamples were generated following the distribution of the sample groupshas been studies in classification [Kim S, et al. J Comp Biology.9(1):127-146] and contrasting with popular error estimates [Braga-Neto UM, et al. Bioinformatics. 20(2):253-258]. The model can be trained usingsubsampling methods [Hua J, et al. EURASIP J Bioinform Syst Biol. 20062006 (1)] and errors of the predictor estimated using the series ofcross validation & sample spreading methods appropriate for smallsamples will be attempted.

Example 4 Pancreatic Cancer

Cell Lines and Xenografts:

MIA PaCa2, BxPC-3, PANC-1, SU.86.86, Hs766T can be purchased from ATCC.Primary tumors can be generated by Ultrasound (US) guided implantationof 5e5 cells into pancreata of nu/nu mice, as in [Huynh A S, et al. PLoSOne. 2011 6(5):e20330], and some of these develop hepatic metastases.

KPC Mouse Model:

There are 15 genetically modified mouse models (GEMM) of PDAC. The KPC(KrasG12D; p53R172H; Pdx1-Cre) GEMM is widely used, well-validated, andclinically relevant [Olive K P, et al. Clin Cancer Res. 200612(18):5277-5287], although tumors in KPC mice are generally resistantto gemcitabine. KPC mice develop a spectrum of premalignant PanINs thatultimately progress to overt carcinoma with 100% penetrance. The tumorsexhibit stromal desmoplasia, similar to human PDAC. Metastases occur inca. 80% of KPC mice, primarily to the liver and lungs. Emergence ofpancreatic lesions measurable by US occurs between 10-16 weeks and theycan be monitored until they reach 200-300 mm³, at which time they can berandomized into study.

pH Imaging with ¹H MRSI of IEPA:

The pHe of tumor and stroma can be measured with ¹H MRSI of theimidazole, IEPA [van Sluis R, et al. Magn Reson Med. 199941(4):743-750]. Outer volume suppression (OVS) can be used withpre-saturation water suppression followed by a standard MRSI pulsesequence to generate spectral maps in approx. 20 min of acquisition.FIG. 26 shows a pH image of MDA-mb-435 xenograft, along with the CSImatrix and a derived relationship between pHe and perfusion from theco-registered images. The spatio-temporal resolution of theseacquisitions can be improved by performing the acquisitions withspectral-spatial pulses for solvent suppression and compressed sensing.It is estimated that the resolution can be reduced to 1×1×1 mm³.

Histological & Immunohistochemical (IHC) Biomarkers:

The following IHC markers can be used. Pimonidazole (Hypoxyprobe®) is anaccepted measure of tissue hypoxia. In hypoxia, cells express hypoxiainducible factor, HIF1-α, which in turn induces expression of glucosetransporter (GLUT-1) (89-91), and carbonic anhydrase IX (CA-IX) (51).GLUT-1 is associated with increased glucose metabolism, FDG uptake andpoor prognosis [Behrooz A, et al. J Bio Chem. 1997 9:5555-5562; AirleyR, et al. Clinical Can Res. 2001 7:928-934; Boado R J, et al. JNeurochemistry. 2002 80(3):552-554]. CA-IX catalyzes the hydration ofCO₂ to acidify the extracellular pH [Turner K, et al. British Journal ofCancer. 2002 1276-1282(86)] and is associated with decreased survival inmultiple cancer types [Turner K, et al. British Journal of Cancer. 20021276-1282(86); Kaluz S, et al. Biochimica et Biophysica Acta.2009:162-172; Koukourakis M I, et al. Clinical Can Res. 20017(3399-3403):3399]. Vasculature is determined with IHC for PECAM-1/CD31.Further, plasma membrane staining for LAMP-2 may be a good marker foracidic pH, and this marker can also be used. Patent vasculature can belabeled by injecting Hoechst 33342 one minute prior to animal sacrifice.Antibody evaluation and optimization of staining protocols have beenpublished [Tafreshi N K, et al. Clin Cancer Res. 2012 18(1):207-219;Tafreshi N K, et al. Cancer Res. 2011 71(3):1050-1059]. Stained slidescan be scanned using the Aperio™ ScanScope XT, as in [Wojtkowiak J W, etal. Cancer Res. 2012 72(16):3938-3947]. Definiens Developer XD can beused to segment up to 100,000 individual cells per slide. The algorithmcan be applied to the entire slide's digital image to segment positivepixels of various intensities. The positivity can be quantified by thenumber and location of cells exhibiting positive stain as a percentageof total tumor cell count, using established thresholds and theresultant “heat maps” can group cells with common phenotypes.

Habitat Imaging.

As disclosed herein, “hypoxic habitats” should result from deficits inperfusion measured with contrast-enhanced MRI, and should have high celldensity and be adjacent to necrotic volumes, as measured by diffusionMRI. An example is shown in FIG. 10, which shows hypoxic (purple) andnecrotic (green) habitats in clinical (panel A) and sub-cutaneous (B)pancreatic cancers. In the animal studies, these data werequantitatively compared to pimonidazole staining as a “gold standard”.MRI is routinely used in the workup of PDAC patients, according to NCCNguidelines. In addition, 18-FDG PET is obtained in the workup of PDACpatients at Moffitt as SOC, and thus, results from this study could haverapid clinical translation.

FDG PET.

PET/CT images can be obtained with a Siemens Inveon.

Hyperpolarized HCO₃ and Other Tracers.

An Oxford HyperSense DNP hyperpolarizer can be used for thehyperpolarization of ¹³C, ³¹P and ¹⁵N containing tracers. Publishedmanuscripts include the use of ¹³C pyruvate to evaluate inhibition ofLDH-A and glutaminase [Dutta P, et al. Cancer Res. 2013] and the use of¹³C fumarate to evaluate the effects of sorafenib and the comparison of¹³C pyruvate metabolism to MRI measured diffusion to monitor anti-cancertherapy response [Zhang X, et al. Proc ISMRM. 2012 15:4049]. Additionsinclude co-polarization of ¹³C NaHCO₃ and ¹³C pyruvic acid (FIG. 27),polarization of ³¹P dimethylmethylphosphonate, DMMP, and polarization of¹⁵N glutamine.

General Protocol for Habitat Imaging:

Tumors are imaged at volumes between 300-500 mm³, first with MRIfollowed by 18-FDG PET. All animals have jugular catheters forreproducible injections and are anesthetized with isoflurane;respiration and temperature is monitored with Small Animal Instruments,Inc (SAII) monitoring system using a pressure sensitive respiration padand a fiber optic rectal thermometer. The catheter can be fit with a 20cm lead with a dead volume of 40 uL, allowing bolus injections of up to160 uL (0.2 mL total). One day after the MRI scan, animals are injectedwith pimonidazole and FDG and imaged with PET using protocols that havebeen published previously [Cardenas-Rodriguez J, et al. Magn ResonImaging. 2012; Morse D L, et al. NMR Biomed. 2007 20(6):602-614; JordanB F, et al. Neoplasia. 2005 7(5):475-485] Immediately after capturingPET images, animals are sacrificed and tumors removed for whole mounthistology and immunohistochemistry.

PET/CT Acquisition and Processing:

Dynamic imaging with Inveon PET/CT can be performed for 60 minutesstarting at the injection of 250 μCi of ¹⁸F-FDG. Animal respiration andtemperature can be monitored with an M2M Biovet system. SUVs arecalculated by dividing the radioactivity per gram of tissues by injecteddose of radioactivity per gram of mouse weight and expressed per 1×1×1mm³ voxel. Time activity curves (TAC) will be generated using 150 secframe durations and uptake rates can be calculated using Patlakanalyses. Input functions, Cp(t), can be determined in a parallel seriesof animals by sampling 0.02 mL from the jugular vein at times throughoutthe 60 min uptake period.

MRI Acquisition and Processing:

Images and spectra can be acquired on a 7 Tesla ASR 310 horizontalsystem (Agilent Technologies, Inc.). Following scout images, animals canbe injected via catheter with a 100 μL bolus followed by slow infusionof 175 mM Na-IEPA, pH 7.2 for the duration of the MRSI acquisition.These conditions yield maximal S/N without affecting the tumor pHthrough excess buffering [van Sluis R, et al. Magn Reson Med. 199941(4):743-750; Gillies R J, et al. IEEE Eng Med Biol Mag. 200423(5):57-64]. Improvements to the MRSI sequence can be made usingspectral-spatial excitation for solvent suppression with Cartesian EPSIreadout. These sequences and reconstruction software give a 16-foldacceleration factor compared to conventional MRSI. The benchmark forthese studies is a SNR for the H2 of IEPA of >5 in 1×1×1 mm isotropicvoxels with 2× zero-filled resolution enhancement within an acquisitiontime of 20 min. A full time series will be obtained as the IEPA infusesinto the tumor to reach steady-state. Following pH imaging, a diffusionseries can be obtained in 10 minutes with 4 b-values (50, 250, 500,700), and a subsequent T₁ series can be obtained during which theanimals are injected with a 0.1 mmol/kg bolus of Magnevist (50 uL+40 uLchase). DCE and DW images can be acquired with 0.25 mm in-plane and 1 mmslice resolution.

Image Co-Registration:

Image data can be imported into Matlab for all analyses. Tumors will besemi-automatically segmented from the T2 and CT images using an ensemblesegmentation approach that was developed [Gu Y, et al. PatternRecognition. 2013 46(3):692-702]. T2 MRI and CT images can be registeredby generating volumetric surface maps from each modality and 3-Drotation to minimize differences using Dice coefficient as a similarityindex. The rotation vectors can then be applied so that PET+CT+MRI areco-registered in subsequent analyses. Because PET resolution is lowest,the CT and MRI data can be down sampled to generate voxels of identicalsize, which is expected to be 1 mm isotropic.

Image Analysis and Algorithm Development:

This Example shows that a combination of contrast-enhanced MRI (as amarker for perfusion), diffusion MRI (as a marker for cellularity) andFDG-PET (as a marker for glycolysis) can be used to predict the pH oftumors. The dynamic contrast series can be analyzed to generate maps ofthe vascular transfer constant, K_(trans), the vascular volume and theextravascular/extracellular volumes. From the K_(trans) maps, volumescan be spatially defined that are well- and poorly-perfused. Thediffusion series can be fit to the Stejskal-Tanner relationship [JordanB F, et al. Neoplasia. 2005 7(5):475-485; Jennings D, et al. Neoplasia.2002 4(3):255-262; Galons J P, et al. Neoplasia. 1999 1(2):113-117] togenerate a 3-D volumetric map of the Apparent Diffusion Coefficient,ADC, using Levenberg-Marquardt nonlinear-least squares fitting [ForoutanP K, et al. PLoS One. 2013; Mignion L, et al. Cancer Res. 2013]. DynamicPET data can be used to generate 3-D maps of per-voxel uptake rates, K,from Patlak analyses. Algorithms can be developed using continuousvariables and, as the algorithms mature, and to make the analyses moreclinically feasible, it is expected to be able to identify cutoffs fordichotomization [Otsu N. IEEE Trans Sys, Man, Cyber. 1979 9(1):62-66].Also, for increased clinical applicability, it will be determinedwhether the same predictive value can be derived from: 15 minute postcontrast enhanced data (ΔT₁), a single (b=500) diffusion weighted image(DWI) and SUV values from the DCE, diffusion and PET data respectively.

“Habitat” Imaging.

Data cubes can be generated for each voxel with values for K_(trans) (orΔT₁), ADC (or DWI) and uptake rates (or SUV). Analyses can be initiatedusing microPD, a suite of algorithms that predicts pharmacokinetics (PK)and pharmacodynamics (PD) using sample-specific histology images asinput to the model [Rejniak K A, et al. Front Oncol. 2013 3:111].Perfusion, cellularity, and glycolysis parameters obtained from each1×1×1 mm³ voxel in the PDAC “habitat” maps, can be used to predict thepH distributions, which are then quantitatively compared to the “groundtruth” pHe values obtained from the ¹H MRSI of IEPA which can beco-registered to the same samples. The model can be iteratively adjustedto maximize the correlation coefficient between predicted and measuredpHe values. Upon completion of the test set, it is expected that theBland-Altman CoV to be less than 15%. Additionally, these analyses canindicate whether there is bias in the errors toward extreme pH values,which are anticipated due to the titration curve of IEPA.

Histology and IHC:

Following imaging, animals can be injected i.v. with Hoechst dye andsacrificed, the tumors tattooed for orientation, formalin fixed andparaffin embedded as whole mounts. Initially, sections can be stainedwith H&E and IHC for pimonidazole and PECAM to identify hypoxia andvasculature, respectively. Fluorescent imaging of Hoechst dye canidentify patent vasculature.

Example 5 Segmenting MRI Images of Brain into 3D Habitat Maps

The information contained in superimposable magnetic resonance imaging(MRI) images acquired using routine pulse sequences can be combined by amethod such as depicted in FIG. 28 so as to permit objective 3Dsegmentation of the images to identify distinct normal and pathologicregions (“habitats”) within organs, tumors and pre-cancerous lesions.This knowledge-driven method can combine information from orthogonal MRIsequences to automatically segment the images into regions correspondingto habitats within organs based on the differential dependence of therelative signal intensity of each habitat on the MRI sequence used. Thismethod will decrease the need for manual interaction to circumscriberegions-of-interest (ROIs) on the images, thereby increasing the speedof analysis. The methodology described here can produce 3D habitat mapsof the analyzed organs which can be used for diagnosis, prognosticationand evaluation of treatment response.

In one embodiment, routine MRI images such as T1-weighted pre-contrast(with or without fat suppression), T1-weighted post-contrast (with orwithout fat suppression), T2-weighted pre-contrast, fluid attenuatedinversion recovery (FLAIR), and Apparent Diffusion Coefficient of water(ADC) maps are examined Rigid-body and/or elastic image registration canbe used to render the images from the different MRI sequencessuperimposable in 3D prior to performing the analysis. Classification ofpixels into clusters at each node in the methodology depicted in FIG. 28can be based on pixel intensity and this classification may be based onOtsu thresholding [Nobuyuki Otsu (1979). IEEE Trans. Sys., Man., Cyber.9(1): 62-66] or another suitable method. For this application, a globalimage thresholding method that segments the image into classes based onthe Otsu method was implemented in MATLAB. The histogram-based methoddetermines the thresholds that minimize the weighted within-classvariance. Given an array of pixel intensities, the method returns amatching array of cluster indices assigned to each pixel. An example ofthe use of this method to objectively identify different normal andpathologic tissue in a patient with Glioblastoma multiforme is depictedin FIG. 29. In particular this methodology can identify 3D volumes inthe images corresponding to increased immune infiltration into the tumorfollowing response to immunotherapy (“treatment-induced changes” in FIG.29).

Example 6 Segmenting MRI Images of Renal Cell Carcinoma into 3D HabitatMaps

In another embodiment, superimposable standard-of-care MRI imagesroutinely prescribed in the setting of renal cell carcinoma can be used,e.g., T1-weighted pre-contrast, T1-weighted post-contrast (typicallymore than one temporal phase), T2-weighted pre-contrast, and ADC maps.Information from superimposable Computed Tomography (CT) images can beincorporated into the decision tree to increase the combined diagnosticpower of the method. Rigid-body and/or elastic image registration can beused to render the images from the different MRI sequences and CTsuperimposable in 3D prior to performing the analysis. Classification ofpixels into clusters can be accomplished analogously to the decisiontree depicted in FIG. 28. This method can separate tumor pixels intodistinct clusters based on the degree of sarcomatoid vs. non-sarcomatoiddifferentiation, and this information can be used to objectivelydetermine the degree of sarcomatoid differentiation in renal cellcarcinoma.

Example 6 Segmenting MRI Images of Soft Tissue Masses into 3D HabitatMaps

In yet another embodiment, superimposable standard-of-care MRI imagesroutinely prescribed in the setting of soft tissue masses of theextremities can be used, e.g., T1-weighted pre-contrast (with or withoutfat suppression), T1-weighted post-contrast (with or without fatsuppression), Short Tau Inversion Recovery (STIR), and ADC maps.Additional sequences, such as T2*-weighted gradient-echo images, ifavailable, can be incorporated into the decision tree to increase thecombined diagnostic power of the method. Rigid-body and/or elastic imageregistration can be used to render the images from the different MRIsequences superimposable in 3D prior to performing the analysis.Classification of pixels into clusters can be accomplished analogouslyto the decision tree depicted in FIG. 28. This method can separate tumorpixels into distinct clusters representing benign or malignanttransformation, and that information can be used for an objectivediagnosis.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meanings as commonly understood by one of skill in the artto which the disclosed invention belongs. Publications cited herein andthe materials for which they are cited are specifically incorporated byreference.

Those skilled in the art will recognize, or be able to ascertain usingno more than routine experimentation, many equivalents to the specificembodiments of the invention described herein. Such equivalents areintended to be encompassed by the following claims.

1. A radiological method for predicting the severity of a tumor in asubject, comprising (a) spatially superimposing two or more radiologicalimages of the tumor sufficient to define regional habitat variations intwo or more ecological dynamics in the tumor, and (b) comparing thehabitat variations to one or more controls to predict the severity ofthe tumor.
 2. The method of claim 1, wherein the tumor is a glioblastomamultiforme (GBM).
 3. The method of claim 1, wherein the tumor isprostate cancer.
 4. The method of claim 1, wherein the tumor is a softtissue sarcoma.
 5. The method of claim 1, wherein the tumor is apancreatic cancer.
 6. The method of claim 1, wherein at least one of thetwo or more ecological dynamics comprises perfusion (blood flow).
 7. Themethod of claim 1, wherein at least one of the two or more ecologicaldynamics comprises interstitial cell density (edema).
 8. The method ofclaim 1, wherein at least one of the two or more ecological dynamicscomprises extracellular pH (pHe).
 9. The method of claim 1, wherein atleast one of the two or more ecological dynamics comprises hypoxia. 10.The method of claim 1, wherein at least one of the two or moreradiological images is obtained by a magnetic resonance imaging (MRI)sequence.
 11. The method of claim 10, wherein the MRI sequence compriseslongitudinal relaxation time (T1)-weighted images.
 12. The method ofclaim 10, wherein the MRI sequence comprises transverse relaxation time(T2)-weighted images.
 13. The method of claim 10, wherein the MRIsequence comprises fluid attenuated inversion recovery (FLAIR).
 14. Themethod of claim 10, wherein the MRI sequence comprises short tauinversion recovery (STIR)
 15. The method of claim 1, wherein theregional habitat variations are defined using a fuzzy clusteringalgorithm analysis of the radiological images.
 16. The method of claim1, wherein the regional habitat variations are defined using athresholding algorithm analysis of the radiological images.
 17. Themethod of claim 1, wherein the method predicts the survival of thesubject based on the severity of the tumor.
 18. The method of claim 1,wherein detection of relatively low heterogeneity in regional habitatsis an indication of low severity of the tumor.
 19. The method of claim1, wherein detection of relatively high heterogeneity in regionalhabitats is an indication of high severity of the tumor.
 20. The methodof claim 1, wherein detection of relatively high cell density andrelatively high perfusion in the tumor is an indication of low severityof the tumor.